CLEAR-IR: Clarity-Enhanced Active Reconstruction of Infrared Imagery
This addresses a domain-specific problem for robotics by enhancing infrared image clarity to support tasks like object detection and tracking in low-light settings, representing an incremental improvement over prior methods.
The paper tackles the problem of active emitter patterns degrading infrared imagery for robotic perception in dark environments, proposing a U-Net-based method that reconstructs clean images and improves downstream tasks, outperforming existing techniques and enabling reliable operation across illumination conditions.
This paper presents a novel approach for enabling robust robotic perception in dark environments using infrared (IR) stream. IR stream is less susceptible to noise than RGB in low-light conditions. However, it is dominated by active emitter patterns that hinder high-level tasks such as object detection, tracking and localisation. To address this, a U-Net-based architecture is proposed that reconstructs clean IR images from emitter-populated input, improving both image quality and downstream robotic performance. This approach outperforms existing enhancement techniques and enables reliable operation of vision-driven robotic systems across illumination conditions from well-lit to extreme low-light scenes.